Search Results (11)

Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in search and rescue (SAR) missions to rapidly locate survivors. However, deploying autonomous swarms in low-altitude airspace shared with crewed rescue aircraft poses significant algorithmic and safety challenges. Following PRISMA 2020 guidelines, this systematic review synthesizes 44 peer-reviewed studies (2022–2026) to evaluate the literature across algorithmic optimization, reality-gap limitations, tactical deconfliction, and validation maturity. The synthesis reveals a consistent trend toward decentralized swarms, driven by Deep Reinforcement Learning in dynamic environments and by bio-inspired metaheuristics for static coverage. Despite these algorithmic advancements, the literature exhibits a severe reality gap: approximately 86% of evaluated models rely exclusively on idealized software simulations, abstracting away critical constraints like communication denial and sensor noise. Furthermore, most models assume uncontested airspace and lack the Manned–Unmanned Teaming (MUM-T) and tactical deconfliction protocols necessary for safe coexistence with rescue helicopters. To achieve true operational readiness within the critical “Golden 72 Hours” of disaster response, the discipline must transition toward hardware-in-the-loop and physical field trials, natively integrating airspace deconfliction into core swarm optimization loops. Full article

Human–machine interaction (HMI) has become a central issue in autonomous driving because partial automation can degrade, rather than improve, human performance during takeover, handover, and out-of-the-loop transitions. Similar interaction risks are emerging in manned–unmanned vehicle teaming (MUM-T), where operators must supervise multiple autonomous or remotely controlled assets under higher mission complexity and safety-critical constraints. However, existing effectiveness analyses of unmanned and MUM-T systems often treat the level of autonomy (LOA) as a fixed system attribute or assume the highest autonomy level, thereby obscuring the human–automation bottlenecks that arise during partial autonomy. This study proposes a function-specific HMI effectiveness framework in which autonomy is represented as a vector across surveillance, maneuver, fire or neutralization, command and control, and human–machine teaming functions. Measures of performance (MOPs) are modeled as conditional performances jointly shaped by function-specific LOA, operational environment, and intrinsic system capability, and are propagated through a five-layer LOA–MOP–MOE structure to a mission-level measure of effectiveness (MOE). The framework is demonstrated using a notional mine countermeasure scenario in which manned minehunters cooperate with unmanned underwater and surface vehicles. Four autonomy progression stages, from manned-centric operation to advanced cooperative autonomy, are evaluated for timely route opening. The case illustrates a non-monotonic partial-autonomy trap, or LOA-2 valley: remotely controlled unmanned assets may temporarily reduce mission effectiveness when teleoperation workload and HMI bottlenecks outweigh equipment gains, before cooperative and supervisory autonomy restore and exceed baseline performance. The contribution of this study lies not in the notional numerical results but in providing an explicit diagnostic structure for identifying where and why function-specific autonomy, control sharing, and HMI bottlenecks shape mission effectiveness. The framework thereby extends human–machine interaction analysis from autonomous driving to the broader, higher-risk setting of manned–unmanned vehicle teaming. Full article

Manned–unmanned teaming (MUMT) represents a critical evolution in collaborative operations across domains including search and rescue, firefighting, surveillance, and defense. Despite widespread interest in MUMT capabilities, the field lacks a unified taxonomy for classifying and comparing system capabilities, hindering systematic development and technology integration. This paper presents a comprehensive framework for MUMT that addresses the fundamental challenge of organizing and assessing cognitive agent capabilities within human–machine teams. Building upon established automation frameworks, we propose a three-dimensional framework comprising information analysis and inference, decision-making, and action execution. Each dimension defines six hierarchical levels of teaming, ranging from human-only operations to fully autonomous cognitive agent capabilities. The framework distinguishes itself from existing taxonomies by explicitly modeling collaborative teaming rather than simple task delegation, incorporating transparency requirements, and addressing dynamic authority relationships between humans and cognitive agents. The proposed taxonomy provides researchers and engineers with a common vocabulary for MUMT development, enables gap analysis for technology roadmaps, and facilitates the identification of integration opportunities across organizational boundaries. Full article

Conventional direct communication in Manned–Unmanned Teaming (MUM-T) suffers from fundamental scalability and security limitations. As the number of Unmanned Aerial Vehicles (UAVs) increases, the communication burden on the manned aircraft (MA) grows significantly, while security threats originating from UAVs may directly propagate to the MA. To address these challenges, this paper proposes a hierarchical communication architecture that introduces dedicated Network Drones (NDs) as intermediate communication mediators and trust boundaries between the MA and multiple UAV swarms. In the proposed design, the MA interacts exclusively with NDs, while UAV swarms communicate through ND-mediated links, effectively bounding the number of MA-facing connections and enabling scalable communication. Building on this structured communication model, a message-level Zero-Trust framework is enforced at the MA–ND interface. Each message is evaluated using a multi-dimensional risk model that incorporates authentication consistency, behavioral consistency, content validity, and contextual information, enabling early detection and containment of compromised UAV behavior. Furthermore, the architecture incorporates backup planning mechanisms, including dynamic reassociation and hot-standby operation, to ensure robust communication under ND failure conditions. Experimental results demonstrate that the proposed approach reduces MA-facing communication overhead, stabilizes end-to-end latency, and improves detection performance in terms of false positives and false negatives, while maintaining system robustness under failure scenarios. Full article

In the design of Infantry Squad Weapon Equipment System-of-Systems (ISWES), traditional text-based systems engineering primarily relies on empirical methods derived from historical designs. This approach suffers from low design efficiency, protracted development cycles, and incomplete system requirements analysis. In addition, determining effective equipment configurations to maximize the integrated operational capabilities of weapon systems has garnered increasing attention. This study proposes a micro-scenario-oriented squad equipment system design framework featuring manned–unmanned teaming collocation optimization. First, an MBSE method applicable to ISWES modeling is proposed, and the initial allocation scheme of ISWES is obtained. Subsequently, a multi-objective optimization model for an allocation scheme is established based on operation loop theory with the objectives of maximizing combat effectiveness and network robustness while minimizing weapon costs, and the CCMO algorithm is employed to obtain the Pareto set. Then, a multi-attribute scheme selection method leveraging Successive Elimination of Alternatives Based on Order and Degree of Efficiency (SEABODE)-improved TOPSIS is proposed to identify the optimal collocation. Finally, a case study on infantry squad-level equipment system design validates the framework’s feasibility and effectiveness. Full article

Unmanned Aerial Vehicles (UAVs) are playing an increasingly vital role in modern battlefields. Accordingly, considerable research has been devoted to Manned–Unmanned Teaming (MUM-T) systems, with formation flight recognized as a key enabling technology for coordinating multiple UAVs. In MUM-T operations, leader–follower formations are commonly employed, while distributed formation methods have gained increasing attention owing to their stability and scalability. Among these, bearing-based control provides unique advantages for managing dynamic formations involving scaling and rotation. However, conventional bearing-based approaches typically require multiple leaders and encounter inherent limitations in flexibly handling obstacle avoidance. To address these challenges, this study proposes a hierarchical bearing-based leader–follower formation system comprising a single leader and multiple follower UAVs. By introducing the concept of virtual leaders, the proposed method enables the construction of formations with only one leader, thereby simplifying the system architecture while preserving scalability. In addition, a novel obstacle-avoidance strategy is developed, allowing followers to avoid collisions efficiently while maintaining formation integrity. The effectiveness of the proposed framework is demonstrated through numerical simulations of representative formation patterns, including V-shaped and hexagonal configurations, in obstacle-rich environments. The results confirm that follower UAVs successfully tracked the leader, preserved the designated formation, and achieved effective obstacle avoidance, thereby validating the stability and robustness of the proposed approach. Full article

The integration of diverse robotic platforms with varying payload capacities is a critical challenge in swarm robotics and autonomous systems. This paper presents a robust, modular framework designed to manage and coordinate heterogeneous swarms of autonomous vehicles, including terrestrial, aerial, and aquatic platforms. Built on the Robot Operating System (ROS) and integrated with C++ and ArduPilot, the framework enables real-time communication, autonomous decision-making, and mission execution across multi-domain environments. Its modular design supports seamless scalability and interoperability, making it adaptable to a wide range of applications. The proposed framework was evaluated through simulations and real-world experiments, demonstrating its capabilities in collision avoidance, dynamic mission planning, and autonomous target reallocation. Experimental results highlight the framework’s robustness in managing UAV swarms, achieving 100% collision avoidance success and significant operator workload reduction, in the tested scenarios. These findings underscore the framework’s potential for practical deployment in applications such as disaster response, reconnaissance, and search-and-rescue operations. This research advances the field of swarm robotics by offering a scalable and adaptable solution for managing heterogeneous autonomous systems in complex environments. Full article

The gradual integration of Manned–Unmanned Teaming (MUM-T) is gaining increasing significance. An intriguing feature is the ability to do relative estimation solely through the use of the INS/GPS system. However, in certain environments, such as GNSS-denied areas, this method may lack the necessary accuracy and reliability to successfully execute autonomous formation flight. In order to achieve autonomous formation flight, we are conducting an initial investigation into the development of a relative estimator and control laws for MUM-T. Our proposal involves the use of a quaternion-based relative state estimator to combine GPS and INS sensor data from each UAV with vision pose estimation of the remote carrier obtained from the fighter. The technique has been validated through simulated findings, which paved the way for the experiments explained in the paper. Full article

Informal soft system methodologies hold a significant role in developing complex systems. They bridge system knowledge and sensemaking among heterogeneous stakeholders. This article investigates the application of conceptual models to support such communication and understanding among transdisciplinary stakeholders, ensuring the translation of customer requirements and needs into suitable engineered systems. This article presents a case study incorporating observations, interviews, and a review of conceptual models utilized by an aerospace and defense case company for the development of future Manned–Unmanned Systems. It explores how practitioners employ conceptual modeling to support the Human Systems Integration (HSI) aspects of technological, organizational, and human elements of Manned–Unmanned Teaming (MUM-T) systems. The results indicate that practitioners utilize a mix of informal and formal types of conceptual models when developing Human Systems Integration aspects of the system. Formal models, such as sequence diagrams, requirement overviews, and functional flow models, are applied when addressing technology-focused aspects. Organization-centered modeling leverages representations like stakeholder maps and swimlane diagrams, while people-centered aspects rely more on informal techniques such as storytelling and user personas. The findings suggest a potential underestimation by practitioners of the value of quantification in conceptual modeling for Manned–Unmanned Systems development. This study highlights the important role that conceptual modeling methods play, particularly focusing on the informal aspects. These methods are instrumental in enhancing effective communication and understanding among transdisciplinary stakeholders. Furthermore, they facilitate mutual understanding, which is essential for fostering collaboration and shared vision in the development of complex systems. This facilitates deeper insights and reasoning into HSI for MUM-T applications. Full article

With the development of intelligent technology, multi-agent systems have been widely applied in military and civilian fields. Compared to a single platform, multi-agent systems can complete more dangerous, difficult, and heavy tasks. However, due to the limited autonomy of unmanned platforms and the regulatory needs of personnel, multi-agent systems cooperating with manned platforms to perform tasks have been more widely promoted at this stage of development. This paper addresses a differential game method for cooperative decision-making of a multi-agent system cooperating with the manned platform for the target-pursuit problem. The manned platform pursues the target according to a certain trajectory, and its state can be obtained by the multi-agent system. Firstly, for the case that the target moves with a fixed trajectory, the target-pursuit problem in a manned–unmanned environment is viewed in the form of game based on a communication graph among agents. Secondly, strategies of all agents are proposed while maintaining their group cohesion. A set of coupled differential equations is solved to implement strategy calculation. Compared to purely unmanned systems, the strategies combine the advantages of the manned platform and add a reference item, which can achieve team cohesion relatively quickly. Furthermore, a brief analysis is made on the scenarios where the target is in another case or adopts other strategies. Finally, comparative simulations have verified the effectiveness and synergy of the strategy. Full article

The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team’s operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of “one spray and three defenses” for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system. Full article